Image processing includes detecting objects in an image so that the image can be say enhanced or the object classified or identified.
Face recognition has become an important area of image processing. The technology for automatically detecting faces in images captured by cameras has many applications, such as biometric access control, security surveillance, image alignment and image tagging.
One of the main challenges in the field of face recognition is the fact the source images are captured by cameras that are not set up in ideal and controllable conditions. The resulting images often suffer from low underlying resolution, blur, large pose variation, low contrast and frequent lighting changes. As a result, the quality of multiple images of the same person may vary widely. This includes variations in (1) image specific quality, such as resolution, sharpness, contrast, compression artefacts and (2) face specific quality such as face geometry, pose, detectable eye and illumination angles.
Most current approaches to face recognition use a reference gallery comprising images of known faces and the aim is to reliably find a face in the gallery that matches the face in the probe image. Of course, the match has to be found even though the face in the gallery and the face in the probe image are captured with different quality, such as resolution, sharpness, contrast, compression artefacts, face geometry, pose, detectable eye and illumination angles.